Movement Of Swarms Research Articles

Collective buoyancy-driven dynamics in swarming enzymatic nanomotors

Enzymatic nanomotors harvest kinetic energy through the catalysis of chemical fuels. When a drop containing nanomotors is placed in a fuel-rich environment, they assemble into ordered groups and exhibit intriguing collective behaviour akin to the bioconvection of aerobic microorganismal suspensions. This collective behaviour presents numerous advantages compared to individual nanomotors, including expanded coverage and prolonged propulsion duration. However, the physical mechanisms underlying the collective motion have yet to be fully elucidated. Our study investigates the formation of enzymatic swarms using experimental analysis and computational modelling. We show that the directional movement of enzymatic nanomotor swarms is due to their solutal buoyancy. We investigate various factors that impact the movement of nanomotor swarms, such as particle concentration, fuel concentration, fuel viscosity, and vertical confinement. We examine the effects of these factors on swarm self-organization to gain a deeper understanding. In addition, the urease catalysis reaction produces ammonia and carbon dioxide, accelerating the directional movement of active swarms in urea compared with passive ones in the same conditions. The numerical analysis agrees with the experimental findings. Our findings are crucial for the potential biomedical applications of enzymatic nanomotor swarms, ranging from enhanced diffusion in bio-fluids and targeted delivery to cancer therapy.

Application of derivative and integral terminal sliding modes in leader-follower type systems"

Subject matter: The study focuses on the control methods for dr20 type robot swarms, specifically on the derivative and integral terminal sliding mode control combined with a nonlinear disturbance observer. The problem of effective swarm control is highly relevant in the current conditions of automation and robotics, especially in the context of performing complex tasks in limited space and in the presence of disturbances. Goal: The development and analysis of a simulation model for the movement of a robot swarm using advanced control methods to ensure system accuracy and stability. The research aims to improve the control methods for robot swarms, enhancing their efficiency and reliability in various operational conditions. Tasks: 1) Develop a simulation model of a robot swarm in the CoppeliaSimEDU environment, considering all necessary parameters for modeling real operating conditions. 2) Implement control algorithms for the leader and followers to maintain the swarm structure and avoid collisions. 3) Conduct a series of experiments to test the effectiveness of the proposed methods, analyzing the results in terms of stability and control accuracy. Methods: Modeling in CoppeliaSimEDU, implementing control algorithms based on derivative and integral terminal sliding mode control, applying a nonlinear disturbance observer to improve system stability. The applied methods allow for the consideration of various disturbances and ensure high control accuracy. Results: he proposed control model allows achieving high following accuracy and collision avoidance even in complex conditions. Experiments have shown that the control methods ensure the stability and accuracy of the robot swarm's movement, reducing the response time to external disturbances. The research results demonstrate that the use of derivative and integral terminal sliding mode control combined with a nonlinear disturbance observer significantly enhances the efficiency of multi-robot systems. Conclusions: The use of advanced control methods significantly improves the efficiency of multi-robot systems, ensuring their reliability and accuracy in real operating conditions. The proposed methods can be applied in various fields where the coordination of a large number of robots is required, including logistics, rescue operations, and environmental monitoring.

Trajectory planning and control of spacecraft avoiding dynamic debris swarm

The continuous accumulation of space debris poses a significant threat to spacecraft on orbit. This paper introduces a dynamic trajectory planning and control framework aimed at ensuring the safe traversal of spacecraft through space debris swarms. Initially, a threat model is established, considering the movement of the debris swarm and the constraints of spacecraft detection range. This model utilizes a dynamically updated, redefined Gaussian mixture model to characterize the threat posed by the debris swarm to spacecraft, with proximity to debris correlating to increased threat levels. Building upon this threat model, four guidance rules are defined, including target guidance, minimal threat, threat threshold, and local path optimization, to facilitate the generation of the shortest collision-free local trajectory for spacecraft. Combining the concept of receding horizon control, an online real-time planning and control framework is formulated for spacecraft navigating through debris swarms. Finally, numerical and semi-physical simulations verify that the proposed framework can stably generate spacecraft safe trajectories and control commands under the threat of numerous dynamic space debris, showing the characteristics of optimal planning trajectory, rapid computation speed and robust safety features.

Multi-scale dynamic imaging reveals that cooperative motility behaviors promote efficient predation in bacteria

Many species, such as fish schools or bird flocks, rely on collective motion to forage, prey, or escape predators. Likewise, Myxococcus xanthus forages and moves collectively to prey and feed on other bacterial species. These activities require two distinct motility machines enabling adventurous (A) and social (S) gliding, however when and how these mechanisms are used has remained elusive. Here, we address this long-standing question by applying multiscale semantic cell tracking during predation. We show that: (1) foragers and swarms can comprise A- and S-motile cells, with single cells exchanging frequently between these groups; (2) A-motility is critical to ensure the directional movement of both foragers and swarms; (3) the combined action of A- and S-motile cells within swarms leads to increased predation efficiencies. These results challenge the notion that A- and S-motilities are exclusive to foragers and swarms, and show that these machines act synergistically to enhance predation efficiency.

Robust distributed control within a curve virtual tube for a robotic swarm under self‐localization drift and precise relative navigation

AbstractTo guide the movement of a robotic swarm in a corridor‐like environment, a curve virtual tube with no obstacle inside was designed in our previous work. This article generalizes the controller design to the condition that all robots have self‐localization drifts and precise relative navigation, where the flocking algorithm is introduced to reduce the negative impact of the self‐localization drift. Similar to the “many wrongs principle,” it is shown that the cohesion behavior and the velocity alignment behavior are able to reduce the influence of the position measurement drift and the velocity measurement error, respectively. For convenience in practical use, a modified vector field controller with five control terms is put forward. Finally, the effectiveness of the proposed method is validated by numerical simulations and real experiments.

Parallel Global Best-Worst Particle Swarm Optimization Algorithm for solving optimization problems

The range of applications of swarm optimization algorithms is very vast. However, high dimensions and more number of decision variables make these optimization problems more complex. Particle Swarm Optimization (PSO) is the most popular optimizer for performing such types of optimization. PSO is motivated from the movement and intelligence of swarms. However, the primary constraint with the PSO and other swarm algorithms is enormous computational time (CT) due to more number of decision variables in complex problem. The number of steps inside Swarm Intelligence Algorithms (SIAs) also increase the complexity of computation in the process of optimization. Many iterations of the procedure of SIA need more CT since these algorithms are iterative in nature. In this study, a new Global Best-Worst Particle Swarm Optimization (GBWPSO) algorithm has been proposed so as to provide a fully version of parallel algorithm. GBWPSO algorithm is the combination of PSO and Jaya algorithm that provides a refined version of parallel algorithm having more parallelism. The proposed algorithm is executed on three different computational hardware with various combinations of population size and maximum number of iteration on five different standard benchmark functions. The evaluation is done on the basis of performance metrics such as speedup (S), real speedup (RS), maximum speedup (MS), efficiency (E), and scalability. The proposed parallel algorithm (P-GBWPSO) outperforms both parallel version of PSO and Jaya algorithm in terms of less CT and better optimal solution. Based on the results, we found that system 03 (S3) is best on proposed GBWPSO algorithm with an efficiency of 1.2518 compare with system 01 (S1) and system 02 (S2).

Discrete Salp Swarm Algorithm for symmetric traveling salesman problem.

In the Salp Swarm Algorithm (SSA), the update mechanism is inspired by the unique chain movement of the salp swarm. Numerous versions of SSA were already put forward to deal with various optimization problems, but there are very few discrete versions among them. d-opt is improved based on the 2-opt algorithm: a decreasing factor d is introduced to control the range of neighborhood search; TPALS are modified by Problem Aware Local Search (PALS) based on the characteristics of Travelling Salesman Problem (TSP); The second leader mechanism increases the randomness of the algorithm and avoids falling into the local optimal solution to a certain extent. We also select six classical crossover operators to experiment and select Subtour Exchange Crossover (SEC) and the above three mechanisms to integrate them into the SSA algorithm framework to form Discrete Salp Swarm Algorithm (DSSA). In addition, DSSA was tested on 23 known TSP instances to verify its performance. Comparative simulation studies with other advanced algorithms are conducted and from the results, it is observed that DSSA satisfactorily solves TSP.

Humidity Control Scheme for Chicken Chick Banda for Improved Performance using Particle Swarm Optimization

The research is about developing of prototype Humidity control unit of a chicken chick Banda for maximum reduction in energy wastage and ensuring conducive environmental condition for bird’s growth and development using the proportional integral differential (PID) controller and the particle swarm optimization (PSO) technique for comparison purposes. The PSO stated here in is a stochastic optimization method working on the movement of swarm so as to achieve convergence. The study is achieved through designing of a prototype of the humidity environment controller to achieve two states or conditions that is for the controlled case and for the uncontrolled case. Environmental humidity control is achieved using a programmed Arduino and the DC FAN. The process is then designed using the MATLAB simulation software operating at the Simulink model designing platform. The same design is connected to the PID controller and then also tuned using the PID tuning platform on the Matlab. The same design is implemented on the workspace using particle swarm optimization method and it is then run to see the system behavior in terms of settling time, rising time and peak overshoot. The major reason of the study is to demystify the myth that one can only use conventional PID controller techniques in performance improvement and that there is a better method which can similarly be used with better results and cheaper. Most poultry farmers are stack with their old ways of achieving good performance therefore the results of this work will be an eye opener for them to embrace new techniques in the market The presented particle swarm optimization techniques shows impressive performance in terms of the settling time, rise time and over shoot.

Simulation of the Movement of a Locust Swarm in Argentina in 2020.

This work presents a simulation for the trajectory of a locust swarm of the species Schistocerca cancellata (Audinet-Serville), between May 22nd and July 29th, 2020, in Argentina. To obtain the directions, temperature, and intensities of the wind, used to determine the daily traveled distance of the insects, the data of weather forecast from Weather Research and Forecasting (WRF) model are used. A statistical analysis shows the effectiveness of the forecast model used in comparison with the real data given by SENASA, which provides latitude and longitude coordinates for the cited period. The results found for the movement of the cloud were satisfactory, they matched with the real data, identifying that temperature and wind speed have a great influence on the movement of locust swarms. The methodology used allows monitoring in real-time their movement, predicting the trajectory and making it possible to plan actions by government control agencies with pesticides in convenient areas.

Comparison of Particle Swarm Optimization and Ziegler Nichols Techniques

Two tuning techniques namely: Particle Swarm Optimization (PSO) and Ziegler Nichols (ZN) technique are compared. PSO is an optimization technique based on the movement and intelligence of swarms. PSO applies the concept of social interaction to problem solving. It is a computational method that optimizes a problem by iteratively trying to improve a candidate solution about a given measure of quality. Ziegler Nichols tuning method is a heuristic method of tuning a PID controller. The ZN close loop tuning is performed by setting the I (integral) and D (derivative) gains to zero and increasing proportional gain to obtain sustained oscillations. The DC Motor is represented by second order transfer function is used as a plant, which is controlled using PID controller. The PID controller parameters are chosen by tuning the controller using PSO algorithm and ZN method. The response of the system to unit step input is plotted and performance measures are evaluated for comparing PSO algorithm and ZN technique. Here we have compared the two tuning methods based upon the settling time (Ts), peak overshoot (Mp) and the two performance indices namely Integral square error (ISE) and Integral Absolute error (IAE).

Particle Swarm Optimization for Solving Economic Load Dispatch Problem

This work deals with the optimization of power flow through interconnected system. The work deals with efficient and reliable evolutionary based approach to solve the economic load dispatch (ELD) with line flows and voltage constraints. The work employs PSO algorithm for ELD. PSO is a robust, stochastic computational technique based on movement and intelligence of swarm. The work introduces a conceptual overview and detailed explanation of PSO algorithm as well as shows how it can be used for solving ELD problems. Inherent shortcoming of the traditional methods of finding the ELD is discussed along with other evolutionary methods of finding ELD. A comparative study of different evolutionary programming technique is done and it is shown that particle swarm optimization offer a better result with greater repeatability and lesser time. The feasibility of the proposed method is demonstrated by using a six generator interconnected system having different cost functions and voltage constraints. The results are compared with those obtained by G.A.

Novel algorithms of particle swarm optimisation with decision criteria

ABSTRACTThe particle swarm optimisation (PSO) is a stochastic, optimisation technique based on the movement and intelligence of swarms. In this paper, three new effective optimisation algorithms BPSO, HPSO and WPSO, by incorporating some decision criteria into PSO, have been proposed and analysed both in terms of their efficiency, resistance to the problem of premature convergence and the ability to avoid local optima. In the new algorithms, for each particle except position, two sets of velocities are generated and the profit matrix is constructed. Using the decision criteria the best strategy is selected. Simulations for benchmark test nonlinear function show that the algorithms in which the decision criteria have been applied, are beneficial over classical PSO in terms of their performance and efficiency.

Instantaneous movement of krill swarms in the Antarctic Circumpolar Current

Antarctic krill are known to have strong swimming capabilities, but direct observations of the speed and direction of krill‐swarm movement within their natural environment are rare. We identified and examined 4060 swarms within the main flow of the Antarctic Circumpolar Current (Scotia Sea) using a combination of an EK60 echosounder, a 153.6 kHz acoustic Doppler current profiler, and ground‐truthing nets. Net displacement magnitude (m) and net angle of deviation (d) were determined by vector subtraction from the background flow immediately below them. Values were compared against control data sets in which swarms were absent. With greater background flow, m became increasingly lower than predicted, which suggests that drag influences swarm movement. The characteristics of the flow regime influenced swarm behavior, given that both m and d varied according to the direction of background flow. Furthermore, multiple‐regression analysis indicated that swarm area, the vicinity of the sea‐ice edge, and salinity had a significant influence on m, with levels of displacement being greatest in larger swarms and in low‐salinity regions close to the ice edge. The ice edge is a key environment for Antarctic krill and swarm behavior may assist in retaining this location. Only fluorescence was found to have a significant influence on d, with deviations being greatest in regions of highest fluorescence. This agrees with laboratory observations of krill turning more frequently within food patches. We demonstrate that it is possible to measure instantaneous movement patterns in Antarctic krill swarms and, at large scales, these movements are consistent with current understanding of responses of krill to local stimuli such as sea‐ice and patches of food.

Design of Optimal Fractional-Order PID Controllers Using Particle Swarm Optimization Algorithm for Automatic Voltage Regulator (AVR) System

In practical applications, the pure derivative action is never used, due to the “derivative kick” produced in the control signal for a step input, and to the undesirable noise amplification. It is usually replaced by a first-order low-pass filter. In this paper, we use a $$\mu $$ -order fractional low-pass filter and define a practical fractional-order controller. The proposed approach with new defined fitness function has very easy implementation and the most control performance. We present a method for optimum tuning of practical fractional PID controllers for automatic voltage regulator system using particle swarm optimization (PSO) algorithm. PSO is a robust stochastic optimization technique based on the movement and intelligence of swarm, applies the concept of social interaction to problem solving. From the comparison this technique with the other methods, its influence and efficiency are illustrated. Simulations and comparisons with other FOPID/PID controllers illustrate that the proposed PSO-FOPID controller can provide good control performance with respect to reference input and also improve the system robustness with respect to model uncertainties.

Nonconvex resource control and lifetime optimization in wireless video sensor networks based on chaotic particle swarm optimization

Wireless video sensor networks (WVSNs) have attracted a lot of interest because of the enhancements that they offer to existing wireless sensor networks applications and their numerous potential in other research areas. However, the introduction of video raises new challenges. The transmission of video and imaging data requires both energy efficiency and quality of service (QoS) assurance in order to ensure the efficient use of sensor resources as well as the integrity of the collected information. To this end, this paper proposes a joint power, rate and lifetime management algorithm in WVSNs based on the network utility maximization framework. The optimization problem is always nonconcave, which makes the problem difficult to solve. This paper makes progress in solving this type of optimization problems using particle swarm optimization (PSO). Based on the movement and intelligence of swarms, PSO is a new evolution algorithm to look for the most fertile feeding location. It can solve discontinuous, nonconvex and nonlinear problems efficiently. First, since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the paper introduces chaos mapping into PSO with adaptive inertia weight factor to avoid the disadvantage of original PSO of easily getting to the local optimal solution in the later evolution period and keep the rapid convergence performance. Second, based on the distribution characteristics of the actual network, we decompose the resource control problem into a number of sub-problems using the hierarchical thought, where each user corresponds to a subsystem which is solved using the proposed CPSO3 method. Through the cooperative coevolution theory, these sub-optimization problems interact with each other to obtain the optimum of the system. Numerical examples show that our algorithm can guarantee fast convergence and fairness within a few iterations. Besides, it is demonstrated that our algorithm can solve the nonconvex optimization problems very efficiently.

Combined Economic Emission Dispatch Problem using Particle Swarm Optimization

ABSTRACT This paper deals with particle swarm optimization (PSO) method to solve Combined Economic emission Dispatch Problem (CEEDP)of thermal units while satisfying the constraints such as generator capacity limits, power balance and line flow limits. PSO is a stochastic optimization process based on the movement and intelligence of swarms. The objective is to minimize the total fuel cost of generation and environmental pollution caused by fossil based thermal generating units. The bi-objective problem is converted into single objective problem by introducing price penalty factor to maintain an acceptable system performance in terms of limits on generator real power outputs, transmission losses with minimum emission dispatch. The proposed approach has been evaluated on an IEEE 30-bus test system with six generators. The results obtained with the proposed approach are compared with results of genetic algorithm and other technique. Keywords

Artistic Exploration of the Worlds of Digital Developmental Swarms

This paper presents artwork that was inspired by a computational model called Swarm Grammars. In this work, the “liveliness” of swarms is combined with the generative capabilities of more established developmental representations. Three of the authors followed their individual artistic approaches to explore the creativity and dynamics of Swarm Grammar structures. One chose to breed structures interactively to compose virtual spaces. The second explores the movement and construction dynamics of interactive swarms. The third artist translated developmental processes of Swarm Grammars into interactions of paint particles driven by friction and gravity.

Survey and Review

The worst traffic jam known to man was probably the one that occurred last August in China. Dubbed the “Mother of All Traffic Jams” by the press, vehicles crawled in stop-and-go traffic for more than 9 days over a stretch of the Beijing Tibet Expressway. It was anything but “express”—the queues of vehicles stretched to over 60 miles. What is even more amazing was the quick appearance, and disappearance, of the congestion. It was as if someone had flipped a switch. This cautionary tale should convince you that modeling traffic is important. It is important to understand what causes traffic jams, and how to best deal with them when they occur. The first article in this issue's Survey and Review section discusses in detail efforts to model traffic. In fact the article goes beyond traffic models, which are basically one-dimensional, to talk about modeling the movement of crowds and swarms, which are two- and three-dimensional. The authors describe three main approaches to modeling traffic and crowds. Since the players in a traffic flow are vehicles, one could consider writing down equations that govern the interactions of these individual “points.” On the other hand, it might be more efficient to view traffic as a “blob” and treat it as a continuum. This second approach uses concepts from gas dynamics. Yet another approach is to incorporate the probabilistic nature of traffic flow at a macroscopic scale and apply ideas from kinetic theory. The paper is up-to-date and self-contained. The authors, Nicola Bellomo and Christian Dogbe, make the material highly accessible. They also provide critical reviews of the different modeling efforts. The second article in this issue concerns robust optimization. This is an important and rapidly developing research area in optimization. The basic premise of robust optimization is to find the best solution to an optimization problem when you are uncertain about the parameters describing the problem. Such problems occur in many applications. For example, when designing a bridge, the uncertainties could be the loads from the vehicles going over it, the wind conditions, the added loads from heavy snow, and the rare occurrence of earthquakes. One is interested in maximizing the strength of the bridge while minimizing the cost under these uncertain conditions. The article, by Dimitris Bertsimas, David Brown, and Constantine Caramanis, offers a gentle introduction to the subject of robust optimization. It leads the reader into the key ideas of the area through an example from financial portfolio optimization, where uncertainty abounds. From there, the paper goes deeper into the mathematical structure of robust optimization problems. Its relation to stochastic programming, where statistics of the uncertainties are available and used, is also explored. The authors also provide an excellent exposition to robust adaptable optimization where the problem involves multistage decisions. The paper contains a fine portfolio of applications of robust optimization. The list of examples is long, and the domains in which they arise very diverse. The paper ends by offering a survey of current research directions. Both papers represent valuable resources to the mathematical sciences. They can be read in depth to gain a full understanding of the intricacies and mathematical richness of these two research areas. The casual reader can also quickly get a view of the research landscape of these fields.

Dynamic Plane Covering by Movement of Swarm

Dynamic Plane Covering by Movement of Swarm

Nonconvex Optimization for Power Control in Wireless CDMA Networks

In this paper, we propose an efficient power control algorithm for the downlink wireless CDMA systems. The goal of our paper is to achieve the optimum and fair resource utilization by maximizing a weighted sum utility with the power constraint. In fact, the objective function in the power optimization problem is always nonconcave, which makes the problem difficult to solve. We make progress in solving this type of optimization problem using PSO (particle swarm optimization). PSO is a new evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which can solve discontinuous, nonconvex and nonlinear problems efficiently. It's proved that the proposed algorithm converges to the global optimal solutions in this paper. Numerical examples show that our algorithm can guarantee the fast convergence and fairness within a few iterations. It also demonstrates that our algorithm can efficiently solve the nonconvex optimization problems when we study the different utility functions in more realistic settings.